HN user

naren87

399 karma

Co-founder of Unstract, an open source no-code LLM platform to automate critical business processes involving unstructured documents.

Visit https://unstract.com to learn more.

Twitter: https://twitter.com/narenism

Posts112
Comments14
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unstract.com 19d ago

What we learned building a multi-agent PDF table extractor

naren87
4pts0
unstract.com 23d ago

We Split PDF Table Extraction Across 6 Agents and a Codegen Step

naren87
2pts0
github.com 3mo ago

Show HN: We built Cursor, but for data transformations [Open Source]

naren87
2pts0
github.com 3mo ago

Show HN: We built Cursor, but for data transformations (open source)

naren87
1pts0
github.com 3mo ago

Show HN: We built Cursor, but for data transformations (open source)

naren87
5pts0
github.com 3mo ago

Show HN: Visitran – Cursor for Data Transformations (open source)

naren87
2pts0
unstract.com 4mo ago

Why LLMs Are Not (Yet) the Silver Bullet for Unstructured Data Processing

naren87
5pts0
github.com 4mo ago

Unstract: Open-source platform to ship document extraction APIs in minutes

naren87
1pts0
unstract.com 5mo ago

PDF Hell: Why is extracting data still a nightmare?

naren87
3pts2
mfbt.ai 5mo ago

Vibe Coding vs. Vibe Engineering

naren87
2pts0
unstract.com 6mo ago

Why LLMs Are Not (Yet) the Silver Bullet for Unstructured Data Processing

naren87
1pts0
github.com 6mo ago

Unstract: Open-source platform to ship document extraction APIs/MCPs in minutes

naren87
2pts0
github.com 7mo ago

Unstract: Open-source platform to ship document extraction APIs in minutes

naren87
1pts0
unstract.com 7mo ago

Specification Grounding: The Missing Link in Vibe Coding

naren87
3pts0
github.com 8mo ago

Unstract: Open-source platform to ship document extraction APIs/MCPs in minutes

naren87
2pts0
github.com 9mo ago

Unstract: Open-source platform to ship document extraction APIs/MCPs in minutes

naren87
1pts0
github.com 10mo ago

Unstract: Open-source platform to ship document extraction APIs/MCPs in minutes

naren87
2pts0
unstract.com 10mo ago

The hard part of document AI isn't OCR – it's schema mapping

naren87
2pts0
github.com 10mo ago

Unstract: Open-source platform to ship document extraction APIs in minutes

naren87
3pts0
github.com 10mo ago

Rubberduck: Emulate OpenAI/Anthropic locally with caching and failure injection

naren87
1pts0
github.com 11mo ago

Unstract: Open-source platform to ship document extraction APIs in minutes

naren87
1pts0
unstract.com 11mo ago

PDF Hell: Why Is Extracting Data Still a Nightmare? (2024)

naren87
3pts0
github.com 11mo ago

Show HN: Rubberduck – Open Source Tool to Emulate OpenAI/Anthropic APIs Locally

naren87
6pts0
unstract.com 11mo ago

The Technical Reason LLMs Fail on Complex Unstructured Data

naren87
1pts0
unstract.com 11mo ago

Specification Grounding: The Missing Link in Vibe Coding

naren87
1pts0
github.com 11mo ago

Unstract: Open-source platform to ship document extraction APIs in minutes

naren87
8pts1
unstract.com 11mo ago

Why PDF Hell Breaks RAG Workflows and What Works

naren87
1pts0
github.com 11mo ago

Rubberduck: Emulate OpenAI/Anthropic locally with caching and failure injection

naren87
8pts0
unstract.com 11mo ago

Why deterministic output from LLMs is nearly impossible

naren87
26pts16
unstract.com 11mo ago

Understanding why deterministic output from LLMs is nearly impossible

naren87
2pts0

Great benchmark! It highlights an important but often downstream problem. In real-world pipelines, the bigger issue comes before this: extracting tables from PDFs or scans without breaking their layout. Once the structure is lost (merged headers, nested cells, footnotes, etc.), no data format can fully recover it.

Check out LLMWhisperer from Unstract —> it preserves table and layout fidelity when converting documents for LLM use. You can try it on complex PDFs or forms here: https://pg.llmwhisperer.unstract.com (no signup needed)

Layout preservation upstream often improves downstream accuracy more than choosing between CSV, JSON, or Markdown. Find more details here: https://unstract.com/llmwhisperer/

@danso: Yes, that is the bio search mode. It is simply a bio search sorted by the number of followers. When you switch to influencer search mode, you see results sorted by shares of the articles related to the keyword you searched for. We actually look for keywords in articles they shared by going through their content with NLP.

The free account allows a lot more searches. Do try.

Hi,

Naren, co-founder of Tweeple Search here. Here are a few points:

1.The Twitter API is not a practical tool you can use to search for tens of thousands of people. Think rate limits. 2. Results there are not sorted by the number of followers (which is decent marker for influence) 3. Check out Tweeple Search's filters, they are very powerful 4. We can give you a list of influencers based on keywords in content they shared as opposed to keywords in their bios (very tough to do this!) 5. Search is only the beginning. Check out our analysis tools

Hello,

I'm one of the co-founders. There is no disagreeing that the search results page is structurally similar to that of Buzzsumo, which is a very good product. We wouldn't have bothered to develop Social Animal if it was going to be just a Buzzsumo clone. We process every article that enters the system through our Natural Language Processing (NLP) engine which figure out the primary and secondary keywords. These keywords are then displayed for every article you see. With this, things get way more interesting:

- You can click on any keyword to display more top performing articles for that keyword

- Just because 2 articles on the same topic perform well, it doesn't mean they are the same. You can compare any 2 articles to see their unique and common keywords. This saves you time, since you almost never have to read those articles.

- Headline analyzer is built in, which you can invoke for any article

- The insights summary feature not only tells you the best time to publish an article for any keyword, it also tells you which title and article lengths work best. There is detailed analysis available as well.

- If you setup keywords, Social Animal will proactively send you every day:

    - Top performing articles for keywords you care about

    - Competitor mentions

    - Potential, new influencers
- You don't have to manually search every day, saving you a ton of time

- You have to use our Influencer search tool to see how good it is :) It is based on a much better algorithm.

- The InspireMe module lets you explore various, related topics for you to get writing inspiration. It's a lot of fun to use with a nice UI.

- The InspireMe module also tells you, for any given keyword, which secondary topics articles are written about so you can find your niche.